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Cited by 29Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

C-NODDI: A constrained NODDI model for axonal density and orientation determinations in cerebral white matter

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Alsameen, Maryam H., Gong, Zhaoyuan, Qian, Wenshu, Kiely, Matthew, Triebswetter, Curtis, Bergeron, Christopher M., Cortina, Luis E., Faulkner, Mary E. +2 more·2023·Frontiers in Neurology
DOI 10.3389/fneur.2023.1205426PMC PMC10435293
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Abstract

Purpose: Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods: Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI. Results: ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM. Conclusion: C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.

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Crossref · 10.3389/fneur.2023.1205426

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  • Multi-shell approaches, including NODDI, constrained NODDI (C-NODDI), and diffusion kurtosis imaging, provide model-dependent indices related to neurite organization, extracellular water, axonal density, and tissue integrity.

    “Multi-shell approaches, including NODDI, constrained NODDI (C-NODDI), and diffusion kurtosis imaging, provide model-dependent indices related to neurite organization, extracellular water, axonal density, and tissue integrity (Alsameen et al., 2023; Raghavan et al., 2021; Schilling et al., 2022).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Alsameen et al., 2023

    The Introduction (page unavailable) describes NODDI and C-NODDI as models yielding neurite-density and orientation indices, with extracellular water included in the diffusion model. The Methods section (page unavailable) reports a multi-shell acquisition. The cited paper does not establish the sentence’s claim about diffusion kurtosis imaging, which is attributed to the citations collectively. (LLM, conf=0.86)

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  1. [1]Peters A. The effects of normal aging on myelin and nerve fibers: a review. J Neurocytol. (2002) 31: 581– 93. doi: 10.1023/A:1025731309829 14501200 · DOI · Paper page
  2. [2]Tang Y Nyengaard JR Pakkenberg B Gundersen HJ. Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol Aging. (1997) 18: 609– 15. doi: 10.1016/S0197-4580(97)00155-3, PMID: 9461058 · DOI
  3. [3]Piguet O Double KL Kril JJ Harasty J Macdonald V McRitchie DA. White matter loss in healthy ageing: a postmortem analysis. Neurobiol Aging. (2009) 30: 1288– 95. doi: 10.1016/j.neurobiolaging.2007.10.015, PMID: 18077060 · DOI
  4. [4]Ghosh N DeLuca G Esiri M. Evidence of axonal damage in human acute demyelinating diseases. J Neurol Sci. (2004) 222: 29– 34. doi: 10.1016/j.jns.2004.03.032 15240192 · DOI
  5. [5]Medana I Esiri M. Axonal damage: a key predictor of outcome in human CNS diseases. Brain. (2003) 126: 515– 30. doi: 10.1093/brain/awg061 12566274 · DOI
  6. [6]Beck D de Lange A-MG Maximov II Richard G Andreassen OA Nordvik JE. White matter microstructure across the adult lifespan: a mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. NeuroImage. (2021) 224: 117441. doi: 10.1016/j.neuroimage.2020.117441, PMID: 33039618 · DOI · Paper page
  7. [7]Qian W Khattar N Cortina LE Spencer RG Bouhrara M. Nonlinear associations of neurite density and myelin content with age revealed using multicomponent diffusion and relaxometry magnetic resonance imaging. NeuroImage. (2020) 223: 117369. doi: 10.1016/j.neuroimage.2020.117369, PMID: 32931942 PMC7775614 · DOI
  8. [9]Su KG Banker G Bourdette D Forte M. Axonal degeneration in multiple sclerosis: the mitochondrial hypothesis. Curr Neurol Neurosci Rep. (2009) 9: 411– 7. doi: 10.1007/s11910-009-0060-3, PMID: 19664372 PMC2839873 · DOI
  9. [10]Kanaan NM Pigino GF Brady ST Lazarov O Binder LI Morfini GA. Axonal degeneration in Alzheimer's disease: when signaling abnormalities meet the axonal transport system. Exp Neurol. (2013) 246: 44– 53. doi: 10.1016/j.expneurol.2012.06.003, PMID: 22721767 PMC3465504 · DOI
  10. [11]Lingor P Koch JC Tönges L Bähr M. Axonal degeneration as a therapeutic target in the CNS. Cell Tissue Res. (2012) 349: 289– 311. doi: 10.1007/s00441-012-1362-3, PMID: 22392734 PMC3375418 · DOI · Paper page
  11. [12]Gong Z Bilgel M Kiely M Triebswetter C Ferrucci L Resnick SM. Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals. Alzheimers Dement. (2023). doi: 10.1002/alz.12968, PMID: 36720000 PMC10387505 · DOI
  12. [13]Inano S Takao H Hayashi N Abe O Ohtomo K. Effects of age and gender on white matter integrity. AJNR Am J Neuroradiol. (2011) 32: 2103– 9. doi: 10.3174/ajnr.A2785, PMID: 21998104 PMC7964377 · DOI · Paper page
  13. [14]Bartzokis G Cummings JL Sultzer D Henderson VW Nuechterlein KH Mintz J. White matter structural integrity in healthy aging adults and patients with Alzheimer disease: a magnetic resonance imaging study. Arch Neurol. (2003) 60: 393– 8. doi: 10.1001/archneur.60.3.393, PMID: 12633151 · DOI
  14. [15]Yeatman JD Wandell BA Mezer AA. Lifespan maturation and degeneration of human brain white matter. Nat Commun. (2014) 5: 4932. doi: 10.1038/ncomms5932, PMID: 25230200 PMC4238904 · DOI
  15. [16]Fjell AM Engvig A Tamnes CK Grydeland H Walhovd KB Westlye LT. Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and Volumetry. Cereb Cortex. (2009) 20: 2055– 68. doi: 10.1093/cercor/bhp280 20032062 · DOI · Paper page
  16. [17]Arshad M Stanley JA Raz N. Adult age differences in subcortical myelin content are consistent with protracted myelination and unrelated to diffusion tensor imaging indices. NeuroImage. (2016) 143: 26– 39. doi: 10.1016/j.neuroimage.2016.08.047, PMID: 27561713 PMC5124541 · DOI
  17. [18]Sullivan EV Pfefferbaum A. Diffusion tensor imaging and aging. Neurosci Biobehav Rev. (2006) 30: 749– 61. doi: 10.1016/j.neubiorev.2006.06.002 16887187 · DOI · Paper page
  18. [19]Saito N Sakai O Ozonoff A Jara H. Relaxo-volumetric multispectral quantitative magnetic resonance imaging of the brain over the human lifespan: global and regional aging patterns. Magn Reson Imaging. (2009) 27: 895– 906. doi: 10.1016/j.mri.2009.05.006, PMID: 19520539 · DOI
  19. [20]Alisch JSR Egan JM Bouhrara M. Differences in the choroid plexus volume and microstructure are associated with body adiposity. Front Endocrinol (Lausanne). (2022) 13: 984929. doi: 10.3389/fendo.2022.984929, PMID: 36313760 PMC9606414 · DOI
  20. [21]Alisch JSR Kiely M Triebswetter C Alsameen MH Gong Z Khattar N. Characterization of age-related differences in the human choroid plexus volume, microstructural integrity, and blood perfusion using multiparameter magnetic resonance imaging. Front Aging Neurosci. (2021) 13: 734992. doi: 10.3389/fnagi.2021.734992, PMID: 34603011 PMC8485051 · DOI
  21. [22]Bouhrara M Avram AV Kiely M Trivedi A Benjamini D. Adult lifespan maturation and degeneration patterns in gray and white matter: a mean apparent propagator (MAP) MRI study. Neurobiol Aging. (2022) 124: 104– 16. doi: 10.1016/j.neurobiolaging.2022.12.016 36641369 PMC9985137 · DOI
  22. [23]Bouhrara M Cortina LE Khattar N Rejimon AC Ajamu S Cezayirli DS. Maturation and degeneration of the human brainstem across the adult lifespan. Aging (Albany NY). (2021) 13: 14862– 91. doi: 10.18632/aging.203183, PMID: 34115614 PMC8221341 · DOI
  23. [24]Bouhrara M Kim RW Khattar N Qian W Bergeron CM Melvin D. Age-related estimates of aggregate g-ratio of white matter structures assessed using quantitative magnetic resonance neuroimaging. Hum Brain Mapp. (2021) 42: 2362– 73. doi: 10.1002/hbm.25372, PMID: 33595168 PMC8090765 · DOI
  24. [25]Bouhrara M Triebswetter C Kiely M Bilgel M Dolui S Erus G. Association of cerebral blood flow with longitudinal changes in cerebral microstructural integrity in the coronary artery risk development in young adults (CARDIA) study. JAMA Netw Open. (2022) 5: e2231189. doi: 10.1001/jamanetworkopen.2022.31189, PMID: 36094503 PMC9468885 · DOI
  25. [26]Faulkner ME Laporte JP Gong Z Akhonda M Triebswetter C Kiely M. Lower myelin content is associated with lower gait speed in cognitively unimpaired adults. J. Gerontol. (2023). doi: 10.1093/gerona/glad080, PMID: 36879434 PMC10395567 · DOI
  26. [27]Kiely M Triebswetter C Cortina LE Gong Z Alsameen MH Spencer RG. Insights into human cerebral white matter maturation and degeneration across the adult lifespan. NeuroImage. (2022) 247: 118727. doi: 10.1016/j.neuroimage.2021.118727, PMID: 34813969 PMC8792239 · DOI
  27. [28]Kiely M Triebswetter C Gong Z Laporte JP Faulkner ME Akhonda M. Evidence of An association between cerebral blood flow and microstructural integrity in normative aging using a holistic MRI approach. J. Magnet. Resonan. Imaging. (2022) 58: 284– 93. doi: 10.1002/jmri.28508 PMC10154435 36326302 · DOI
  28. [29]Laporte JP Faulkner ME Gong Z Akhonda M Ferrucci L Egan JM. Hypertensive adults exhibit lower myelin content: a multicomponent Relaxometry and diffusion magnetic resonance imaging study. Hypertension. (2023) doi: 10.1161/HYPERTENSIONAHA.123.21012 PMC10355798 37283066 · DOI
  29. [30]Cortina LE Kim RW Kiely M Triebswetter C Gong Z Alsameen MH. Cerebral aggregate g-ratio mapping using magnetic resonance relaxometry and diffusion tensor imaging to investigate sex and age-related differences in white matter microstructure. Magn Reson Imaging. (2022) 85: 87– 92. doi: 10.1016/j.mri.2021.10.019, PMID: 34678436 PMC8629921 · DOI
  30. [31]Walker KA Duggan MR Gong Z Dark HE Laporte JP Faulkner ME. MRI and fluid biomarkers reveal determinants of myelin and axonal loss with aging. Ann. Clin. Transl. Neurol. (2023) 10: 397– 407. doi: 10.1002/acn3.51730, PMID: 36762407 PMC10014005 · DOI
  31. [33]Timmers I Roebroeck A Bastiani M Jansma B Rubio-Gozalbo E Zhang H. Assessing microstructural substrates of white matter abnormalities: a comparative study using DTI and NODDI. PLoS One. (2016) 11: e0167884. doi: 10.1371/journal.pone.0167884, PMID: 28002426 PMC5176300 · DOI
  32. [34]Timmers I Zhang H Bastiani M Jansma BM Roebroeck A Rubio-Gozalbo ME. White matter microstructure pathology in classic galactosemia revealed by neurite orientation dispersion and density imaging. J Inherit Metab Dis. (2015) 38: 295– 304. doi: 10.1007/s10545-014-9780-x, PMID: 25344151 PMC4341012 · DOI
  33. [36]Churchill NW Caverzasi E Graham SJ Hutchison MG Schweizer TA. White matter microstructure in athletes with a history of concussion: comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Hum Brain Mapp. (2017) 38: 4201– 11. doi: 10.1002/hbm.23658, PMID: 28556431 PMC6866962 · DOI · Paper page
  34. [37]Churchill NW Caverzasi E Graham SJ Hutchison MG Schweizer TA. White matter during concussion recovery: comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Hum Brain Mapp. (2019) 40: 1908– 18. doi: 10.1002/hbm.24500, PMID: 30585674 PMC6865569 · DOI · Paper page
  35. [38]By S Xu J Box BA Bagnato FR Smith SA. Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients. Neuroimage Clin. (2017) 15: 333– 42. doi: 10.1016/j.nicl.2017.05.010, PMID: 28560158 PMC5443965 · DOI
  36. [39]Adluru G Gur Y Anderson JS Richards LG Adluru N DiBella EV. Assessment of white matter microstructure in stroke patients using NODDI. Conf Proc IEEE Eng Med Biol Soc. (2014) 2014: 742– 5. doi: 10.1109/EMBC.2014.6943697 PMC4440535 25570065 · DOI · Paper page
  37. [40]Winston GP Micallef C Symms MR Alexander DC Duncan JS Zhang H. Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Res. (2014) 108: 336– 9. doi: 10.1016/j.eplepsyres.2013.11.004, PMID: 24315018 PMC3969285 · DOI · Paper page
  38. [42]Kunz N Zhang H Vasung L O'Brien KR Assaf Y Lazeyras F. Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. NeuroImage. (2014) 96: 288– 99. doi: 10.1016/j.neuroimage.2014.03.057, PMID: 24680870 · DOI
  39. [43]Billiet T Vandenbulcke M Madler B Peeters R Dhollander T Zhang H. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging. (2015) 36: 2107– 21. doi: 10.1016/j.neurobiolaging.2015.02.029, PMID: 25840837 · DOI · Paper page
  40. [44]Chang YS Owen JP Pojman NJ Thieu T Bukshpun P Wakahiro ML. White matter changes of neurite density and Fiber orientation dispersion during human brain maturation. PLoS One. (2015) 10: e0123656. doi: 10.1371/journal.pone.0123656, PMID: 26115451 PMC4482659 · DOI · Paper page
  41. [45]Nazeri A Chakravarty MM Rotenberg DJ Rajji TK Rathi Y Michailovich OV. Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan. J Neurosci. (2015) 35: 1753– 62. doi: 10.1523/JNEUROSCI.3979-14.2015, PMID: 25632148 PMC4308611 · DOI · Paper page
  42. [46]Merluzzi AP Dean DC 3rd Adluru N Suryawanshi GS Okonkwo OC Oh JM. Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging. Neurobiol Aging. (2016) 43: 79– 88. doi: 10.1016/j.neurobiolaging.2016.03.026, PMID: 27255817 PMC4893194 · DOI · Paper page
  43. [47]Kodiweera C Alexander AL Harezlak J McAllister TW Wu YC. Age effects and sex differences in human brain white matter of young to middle-aged adults: a DTI, NODDI, and q-space study. NeuroImage. (2016) 128: 180– 92. doi: 10.1016/j.neuroimage.2015.12.033, PMID: 26724777 PMC4824064 · DOI · Paper page
  44. [51]Motovylyak A Vogt NM Adluru N Ma Y Wang R Oh JM. Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer’s disease. Aging Brain. (2022) 2: 100030. doi: 10.1016/j.nbas.2022.100030, PMID: 36908893 PMC9999444 · DOI
  45. [52]Raghavan S Reid RI Przybelski SA Lesnick TG Graff-Radford J Schwarz CG. Diffusion models reveal white matter microstructural changes with ageing, pathology and cognition. Brain Commun. (2021) 3. doi: 10.1093/braincomms/fcab106 PMC8202149 34136811 · DOI · Paper page
  46. [53]Raghavan S Przybelski SA Reid RI Lesnick TG Ramanan VK Botha H. White matter damage due to vascular, tau, and TDP-43 pathologies and its relevance to cognition. Acta Neuropathol Commun. (2022) 10: 16. doi: 10.1186/s40478-022-01319-6, PMID: 35123591 PMC8817561 · DOI
  47. [54]Slattery CF Zhang J Paterson RW Foulkes AJM Carton A Macpherson K. ApoE influences regional white-matter axonal density loss in Alzheimer's disease. Neurobiol Aging. (2017) 57: 8– 17. doi: 10.1016/j.neurobiolaging.2017.04.021, PMID: 28578156 PMC5538347 · DOI · Paper page
  48. [55]Lawrence KE Nabulsi L Santhalingam V Abaryan Z Villalon-Reina JE Nir TM. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: a UK biobank study. Brain Imaging Behav. (2021) 15: 2813– 23. doi: 10.1007/s11682-021-00548-y, PMID: 34537917 PMC8761720 · DOI
  49. [56]Gong T Tong Q He H Sun Y Zhong J Zhang H. MTE-NODDI: multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times. NeuroImage. (2020) 217: 116906. doi: 10.1016/j.neuroimage.2020.116906, PMID: 32387626 · DOI · Paper page
  50. [58]MacKay A Whittall K Adler J Li D Paty D Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med. (1994) 31: 673– 7. doi: 10.1002/mrm.1910310614 8057820 · DOI
  51. [59]MacKay AL Laule C. Magnetic resonance of myelin water: An in vivo marker for myelin. Brain Plasticity. (2016) 2: 71– 91. doi: 10.3233/BPL-160033, PMID: 29765849 PMC5928546 · DOI
  52. [60]Alonso-Ortiz E Levesque IR Pike GB. MRI-based myelin water imaging: a technical review. Magn Reson Med. (2015) 73: 70– 81. doi: 10.1002/mrm.25198, PMID: 24604728 · DOI
  53. [61]Does MD. Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage. (2018) 182: 136– 48. doi: 10.1016/j.neuroimage.2017.12.087, PMID: 29305163 PMC6028317 · DOI
  54. [63]Kölliker Frers RA Otero-Losada M Kobiec T Udovin LD Aon Bertolino ML Herrera MI. Multidimensional overview of neurofilament light chain contribution to comprehensively understanding multiple sclerosis. Front Immunol. (2022) 13: 912005. doi: 10.3389/fimmu.2022.912005, PMID: 35967312 PMC9368191 · DOI
  55. [64]Yik JT Becquart P Gill J Petkau J Traboulsee A Carruthers R. Serum neurofilament light chain correlates with myelin and axonal magnetic resonance imaging markers in multiple sclerosis. Mult Scler Relat Disord. (2022) 57: 103366. doi: 10.1016/j.msard.2021.103366, PMID: 35158472 · DOI
  56. [65]Wang Z Wang R Li Y Li M Zhang Y Jiang L. Plasma Neurofilament light chain as a predictive biomarker for post-stroke cognitive impairment: a prospective cohort study. Frontiers in aging. Neuroscience. (2021) 13: 13. doi: 10.3389/fnagi.2021.631738 PMC7933545 33679379 · DOI
  57. [66]Khalil M Pirpamer L Hofer E Voortman MM Barro C Leppert D. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun. (2020) 11: 812. doi: 10.1038/s41467-020-14612-6, PMID: 32041951 PMC7010701 · DOI · Paper page
  58. [67]O'Brien RJ Resnick SM Zonderman AB Ferrucci L Crain BJ Pletnikova O. Neuropathologic studies of the Baltimore longitudinal study of aging (BLSA). J Alzheimers Dis. (2009) 18: 665– 75. doi: 10.3233/JAD-2009-1179, PMID: 19661626 PMC2978421 · DOI · Paper page
  59. [68]Bouhrara M Reiter DA Maring MC Bonny JM Spencer RG. Use of the NESMA filter to improve myelin water fraction mapping with brain MRI. J. Neuroimaging. (2018) 28: 640– 9. doi: 10.1111/jon.12537, PMID: 29999204 PMC6212320 · DOI
  60. [69]Veraart J Novikov DS Fieremans E. TE dependent diffusion imaging (TEdDI) distinguishes between compartmental T(2) relaxation times. NeuroImage. (2018) 182: 360– 9. doi: 10.1016/j.neuroimage.2017.09.030, PMID: 28935239 PMC5858973 · DOI · Paper page
  61. [70]Waterfall JJ Casey FP Gutenkunst RN Brown KS Myers CR Brouwer PW. Sloppy-model universality class and the Vandermonde matrix. Phys Rev Lett. (2006) 97. doi: 10.1103/PhysRevLett.97.150601, PMID: 17155311 · DOI · Paper page
  62. [71]Transtrum MK Machta BB Sethna JP. Why are nonlinear fits to data so challenging? Phys Rev Lett. (2010) 104: 060201. doi: 10.1103/PhysRevLett.104.060201, PMID: 20366807 · DOI
  63. [72]Bouhrara M Reiter DA Celik H Fishbein KW Kijowski R Spencer RG. Analysis of mcDESPOT- and CPMG-derived parameter estimates for two-component nonexchanging systems. Magn Reson Med. (2016) 75: 2406– 20. doi: 10.1002/mrm.25801, PMID: 26140371 PMC5958911 · DOI
  64. [74]Zhang Y Brady M Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. (2001) 20: 45– 57. doi: 10.1109/42.906424, PMID: 11293691 · DOI
  65. [75]Jenkinson M Beckmann CF Behrens TE Woolrich MW Smith SM. FSL. FSL Neuroimage. (2012) 62: 782– 90. doi: 10.1016/j.neuroimage.2011.09.015 21979382 · DOI · Paper page
  66. [76]Kazemi K Noorizadeh N. Quantitative comparison of SPM, FSL, and Brainsuite for brain MR image segmentation. J Biomed Phys Eng. (2014) 4: 13– 26. PMID: 25505764 PMC4258855
  67. [77]Hore P Hall LO Goldgof DB Gu Y Maudsley AA Darkazanli A. A scalable framework for segmenting magnetic resonance images. J Signal Process Syst. (2009) 54: 183– 203. doi: 10.1007/s11265-008-0243-1 20046893 PMC2771942 · DOI
  68. [78]Bouix S Martin-Fernandez M Ungar L Nakamura M Koo MS McCarley RW. On evaluating brain tissue classifiers without a ground truth. NeuroImage. (2007) 36: 1207– 24. doi: 10.1016/j.neuroimage.2007.04.031, PMID: 17532646 PMC2702211 · DOI
  69. [79]Mohammadi S Möller HE Kugel H Müller DK Deppe M. Correcting eddy current and motion effects by affine whole-brain registrations: evaluation of three-dimensional distortions and comparison with slicewise correction. Magn Reson Med. (2010) 64: 1047– 56. doi: 10.1002/mrm.22501, PMID: 20574966 · DOI
  70. [80]Benjamini Y. Discovering the false discovery rate. J R Stat Soc Series B. (2010) 72: 405– 16. doi: 10.1111/j.1467-9868.2010.00746.x · DOI
  71. [81]Benjamini Y Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B. (1995) 57: 289– 300. doi: 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
  72. [82]Golub M Neto Henriques R Gouveia NR. Free-water DTI estimates from single b-value data might seem plausible but must be interpreted with care. Magn Reson Med. (2021) 85: 2537– 51. doi: 10.1002/mrm.28599, PMID: 33270935 · DOI
  73. [83]Tristán-Vega A París G de Luis-García R Aja-Fernández S. Accurate free-water estimation in white matter from fast diffusion MRI acquisitions using the spherical means technique. Magn Reson Med. (2022) 87: 1028– 35. doi: 10.1002/mrm.28997 34463395 · DOI
  74. [84]Faiyaz A Doyley M Schifitto G Zhong J Uddin MN. Single-shell NODDI using dictionary-learner-estimated isotropic volume fraction. NMR Biomed. (2022) 35: e4628. doi: 10.1002/nbm.4628, PMID: 34642974 · DOI
  75. [85]Parker D Ould Ismail AA Wolf R Brem S Alexander S Hodges W. Freewater estimator using interpolated initialization (FERNET): characterizing peritumoral edema using clinically feasible diffusion MRI data. PLoS One. (2020) 15: e0233645. doi: 10.1371/journal.pone.0233645, PMID: 32469944 PMC7259683 · DOI
  76. [86]Cox SR Ritchie SJ Tucker-Drob EM Liewald DC Hagenaars SP Davies G. Ageing and brain white matter structure in 3,513 UK biobank participants. Nat Commun. (2016) 7: 13629. doi: 10.1038/ncomms13629, PMID: 27976682 PMC5172385 · DOI · Paper page
  77. [87]Westlye LT Walhovd KB Dale AM Bjørnerud A Due-Tønnessen P Engvig A. Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb Cortex. (2010) 20: 2055– 68. doi: 10.1093/cercor/bhp280, PMID: 20032062 · DOI · Paper page
  78. [88]Bouhrara M Rejimon AC Cortina LE Khattar N Bergeron CM Ferrucci L. Adult brain aging investigated using BMC-mcDESPOT based myelin water fraction imaging. Neurobiol Aging. (2020) 85: 131– 9. doi: 10.1016/j.neurobiolaging.2019.10.003, PMID: 31735379 PMC6924176 · DOI
  79. [89]Dvorak AV Swift-LaPointe T Vavasour IM Lee LE Abel S Russell-Schulz B. An atlas for human brain myelin content throughout the adult life span. Sci Rep. (2021) 11: 269. doi: 10.1038/s41598-020-79540-3, PMID: 33431990 PMC7801525 · DOI
  80. [92]Simons M Nave K-A. Oligodendrocytes: Myelination and axonal support. Cold Spring Harb Perspect Biol. (2015) 8: a020479. doi: 10.1101/cshperspect.a020479 26101081 PMC4691794 · DOI
  81. [93]Lampinen B Szczepankiewicz F Mårtensson J van Westen D Sundgren PC Nilsson M. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: a model comparison using spherical tensor encoding. NeuroImage. (2017) 147: 517– 31. doi: 10.1016/j.neuroimage.2016.11.053, PMID: 27903438 · DOI · Paper page
  82. [94]Kaden E Kelm ND Carson RP Does MD Alexander DC. Multi-compartment microscopic diffusion imaging. NeuroImage. (2016) 139: 346– 59. doi: 10.1016/j.neuroimage.2016.06.002, PMID: 27282476 PMC5517363 · DOI · Paper page
  83. [95]Edwards LJ Pine KJ Ellerbrock I Weiskopf N Mohammadi S. NODDI-DTI: estimating neurite orientation and dispersion parameters from a diffusion tensor in healthy white matter. Front Neurosci. (2017) 11: 720. doi: 10.3389/fnins.2017.00720, PMID: 29326546 PMC5742359 · DOI
  84. [96]Jelescu IO Veraart J Fieremans E Novikov DS. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR Biomed. (2016) 29: 33– 47. doi: 10.1002/nbm.3450, PMID: 26615981 PMC4920129 · DOI · Paper page
  85. [97]Jelescu IO Palombo M Bagnato F Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods. (2020) 344: 108861. doi: 10.1016/j.jneumeth.2020.108861, PMID: 32692999 PMC10163379 · DOI
  86. [98]Lampinen B Szczepankiewicz F Mårtensson J van Westen D Hansson O Westin CF. Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding. Magn Reson Med. (2020) 84: 1605– 23. doi: 10.1002/mrm.28216, PMID: 32141131 PMC7652211 · DOI · Paper page
  87. [99]Lampinen B Szczepankiewicz F Novén M van Westen D Hansson O Englund E. Searching for the neurite density with diffusion MRI: challenges for biophysical modeling. Hum Brain Mapp. (2019) 40: 2529– 45. doi: 10.1002/hbm.24542, PMID: 30802367 PMC6503974 · DOI · Paper page
  88. [100]Fjell AM Walhovd KB Westlye LT Ostby Y Tamnes CK Jernigan TL. When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies. NeuroImage. (2010) 50: 1376– 83. doi: 10.1016/j.neuroimage.2010.01.061, PMID: 20109562 · DOI
  89. [101]Jones DK Horsfield MA Simmons A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med. (1999) 42: 515– 25. doi: 10.1002/(SICI)1522-2594(199909)42:3<515::AID-MRM14>3.0.CO;2-Q, PMID: 10467296 · DOI
  90. [102]Benjamini D Basser PJ. Use of marginal distributions constrained optimization (MADCO) for accelerated 2D MRI relaxometry and diffusometry. J Magnet Res. (1997) 271: 40– 5. doi: 10.1016/j.jmr.2016.08.004 PMC5026962 27543810 · DOI
  91. [103]Veraart J Novikov DS Fieremans E. TE dependent diffusion imaging (TEdDI) distinguishes between compartmental T2 relaxation times. NeuroImage. (2018) 182: 360– 9. doi: 10.1016/j.neuroimage.2017.09.030, PMID: 28935239 PMC5858973 · DOI · Paper page

Cited by

Sorted by citations

Authors
Alsameen, M. H., Gong, Z., Qian, W., Kiely, M., Triebswetter, C., Bergeron, C. M., Cortina, L. E., Faulkner, M. E., Laporte, J. P., & Bouhrara, M.
Affiliations
National Institutes of Health (US)
Corresponding author
Bouhrara, Mustapha
ORCID
Venue
Frontiers in Neurology
Publisher
Frontiers Media SA
Published
2023-08-03
Volume
14
Pages
1205426-1205426
ISSN
1664-2295 (ISSN-L)
DOI
10.3389/fneur.2023.1205426
PMC
PMC10435293
OpenAlex
W4385507168
Cited by
29
References
11
Citations by year
2026: 9 · 2025: 8 · 2024: 8 · 2023: 4
FWCI
4.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Neurology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
NODDI, neurite density index, orientation dispersion index, white matter, axon density, echo time dependence, neurofilament light chain, axonal degeneration, age-related white matter changes, axonal maturation

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